Hydrograph-based storm sewer design optimization by genetic algorithm

2006 ◽  
Vol 33 (3) ◽  
pp. 319-325 ◽  
Author(s):  
M H Afshar ◽  
A Afshar ◽  
M A Mariño ◽  
A A.S Darbandi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Environmental Protection Agency ◽  
Optimal Solution ◽  
Storm Water ◽  
Storm Water Management Model ◽  
Decision Variables ◽  
Proposed Model ◽  
The Us ◽  
Hydraulic Simulator

A model is developed for the optimal design of storm water networks. The model uses a genetic algorithm (GA) as the search engine and the TRANSPORT module of the US Environmental Protection Agency storm water management model version 4.4H (SWMM4.4H) as the hydraulic simulator. Two different schemes are used to formulate the problem with varying degrees of success in reaching a near-optimal solution. In the first scheme, the nodal elevations and pipe diameters are selected as the decision variables of the problem which were determined by the GA to produce the trial solutions. In the second scheme, only nodal elevations are optimized by the GA, and determination of pipe diameters is left to the TRANSPORT SWMM module. Simulation of the trial solutions in both methods is carried out by the TRANSPORT module of SWMM4.4H. The proposed model is applied to some benchmark examples, and the results are presented and compared with the existing results in the literature.Key words: genetic algorithm, optimal design, sewer network, SWMM.

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals Research on the Construction Method of the Service-Oriented Web-SWMM System

2019 ◽  
Vol 8 (6) ◽  
pp. 268 ◽  
Author(s):  
Dawei Xiao ◽  
Min Chen ◽  
Yuchen Lu ◽  
Songshan Yue ◽  
Tao Hou
Keyword(s):  
Environmental Protection Agency ◽  
Global Scale ◽  
Management Model ◽  
Urban Flooding ◽  
Rainfall Runoff ◽  
Protection Agency ◽  
Runoff Simulation ◽  
Storm Water Management Model ◽  
The Us ◽  
Service Oriented

On a global scale, with the acceleration of urbanization and the continuous expansion of cities, the problem of urban flooding has become increasingly prominent. An increasing number of experts and scholars have begun to focus on this phenomenon and build corresponding models to solve the problem. The storm water management model 5 (SWMM5) is a dynamic rainfall-runoff simulation model developed by the US Environmental Protection Agency (EPA); this model simulates urban flooding and drainage well and is widely favored by researchers. However, the use of SWMM5 is relatively cumbersome and limited by the operational platform, and these factors hinder the further promotion and sharing of SWMM5. Based on the OpenGMS platform, this study first encapsulates, deploys, and publishes SWMM5 and further builds the Web-SWMM system for the model. With Web-SWMM, the user can conveniently use network data resources online and call SWMM5 to carry out calculations, avoiding the difficulties caused by the localized use of SWMM5 and enabling the sharing and reuse of SWMM5.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

scholarly journals Using system simulation to search for the optimal multi-ordering policy for perishable goods

2019 ◽  
Vol 7 (1) ◽  
pp. 49
Author(s):  
Y.C. Huang ◽  
X.Y. Chang ◽  
Y.A. Ding
Keyword(s):  
Stochastic Demand ◽  
Optimal Solution ◽  
Practical Applications ◽  
Ordering Policy ◽  
Perishable Goods ◽  
Decision Variables ◽  
Proposed Model ◽  
The Cost ◽  
Optimal Ordering ◽  
Computerized System

<p>This paper explores the possibility that perishable goods can be ordered several times in a single period after considering the cost of Marginal contribution, Marginal loss, Shortage, and Purchasing under stochastic demand. In order to determine the optimal ordering quantity to improve the traditional newsvendor and maximize the total expected profits, and then sensitivity analysis is taken to realize the influence of the parameters on total expected profits and decision variables respectively. In addition, this paper designed a multi-order computerized system with Monte Carlo method to solve the optimal solution under stochastic demand. Based on numerical examples, this paper verified the feasibility and efficiency of the proposed model. Finally, several specific conclusions are drawn for practical applications and future studies.</p>

scholarly journals Calibração do Storm Water Management Model (SWMM) utilizando algoritmos evolucionários multiobjetivo

2016 ◽  
Vol 21 (4) ◽  
pp. 697-707 ◽  
Author(s):  
Klebber Teodomiro Martins Formiga ◽  
Maira de Carvalho ◽  
Karla Alcione Silva ◽  
Alexandre Kepler Soares
Keyword(s):  
Water Management ◽  
Genetic Algorithm ◽  
Reference Point ◽  
Rio Grande ◽  
Storm Water ◽  
Management Model ◽  
Rio Grande Do Sul ◽  
Storm Water Management ◽  
Storm Water Management Model ◽  
Santa Maria

RESUMO O estudo teve por objetivo a realização da calibração do modelo hidrológico Storm Water Management Model (SWMM) para a Bacia Hidrográfica do Arroio Cancela, localizada em Santa Maria, Rio Grande do Sul, utilizando o algoritmo evolucionário multiobjetivo R-NSGA. Para tanto, foram realizadas modificações na estrutura do SWMM, de modo que permitisse seu acoplamento como Evolucionary Reference Point Based Non-Dominated Sorting Genetic Algorithm (R-NSGA) em ambiente de programação MATLAB. As funções objetivo utilizadas foram o Coeficiente de Eficiência de Nash-Sutcliffe (COE), o Erro da Vazão de Pico (EQP) e o Erro do Volume Escoado (EVOL) aplicadas simultaneamente na calibração do modelo. Foi proposto um método para determinação da maior compatibilidade de modo a elencar as melhores soluções. Os resultados dos parâmetros calibrados do SWMM foram próximos aos valores físicos da bacia, com exceção dos valores relativos à equação de Horton. As soluções de maior compatibilidade apresentam um melhor comportamento para os eventos de validação, evidenciando a importância da otimização multiobjetivo.

Study on Regional Traffic Control Optimization Based on State Analysis

2010 ◽  
Vol 44-47 ◽  
pp. 3959-3964
Author(s):  
Liang Zhi Zhang ◽  
Lei Jia ◽  
Mi Nai He
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Traffic Control ◽  
User Equilibrium ◽  
Solution Procedure ◽  
Design Parameters ◽  
Signal Control ◽  
Control Optimization ◽  
Proposed Model ◽  
Control Scheme

The aim of regional traffic control optimization is to find the optimal design parameters while thinking over the route choice of users. This problem can be formulated as a bi-level programming program. In the program, signal control scheme and user equilibrium traffic assignment are optimized in the upper and lower level respectively. The solution procedure developed with the genetic algorithm has been tested with an example of factual road network.Numerical experiment verified the proposed model is quite promising for use in design of regional signal control.

scholarly journals An Integrated Multi-Echelon Supply Chain Network Design Considering Stochastic Demand: A Genetic Algorithm Based Solution

2017 ◽  
Vol 29 (4) ◽  
pp. 391-400 ◽  
Author(s):  
Sara Nakhjirkan ◽  
Farimah Mokhatab Rafiei
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Large Scale ◽  
Human Life ◽  
Optimal Solution ◽  
Green Supply Chain ◽  
Inventory Routing ◽  
Model Object ◽  
Proposed Model ◽  
Practical Tool

The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing–location–inventoryin green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.

Optimal Design of Gears by Means of Genetic Algorithm and Neural Network

2013 ◽  
Vol 397-400 ◽  
pp. 816-820
Author(s):  
Yong Gang Li, ◽  
Yong Mei Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Relative Parameter

Optimal design of gears was complicated with much difficulty to determine the parameter of strength constraint equation, and find the optimal solution. Used BP Neural Network to approximate the relative parameter of gears optimization design which was shown by chart. Used Genetic Algorithm to search the optimal solution. The result shows that the application of Genetic Algorithm and Neural Network in gear optimization is effective.

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